Revealing the role of explainable AI: How does updating AI applications generate agility-driven performance?

Abstract

This study examines the role of explainable AI in the relationship between updating a portfolio of AI applications and performance. Updating a portfolio of AI applications relates to a firm's dynamic capability to respond to the changes of the market. We propose a set of four hypotheses in which the impact of updating the portfolio of AI applications on firm performance is mediated by business process agility. Additionally, we suggest that building explainability into the portfolio of AI applications when firms update their portfolio acts as a moderator to generate greater agility, which in return improves overall firm performance. Data from a survey of IT executives were analyzed to validate our proposed hypotheses. The results from the survey were also evaluated by a qualitative focus group session. This research is a significant contribution to the emerging literature on dynamic capabilities as it presents and tests a theory regarding the influence of the firm capability to update the portfolio of AI applications on agility and firm performance and the role of explainable AI.